Create projects as quickly
as news breaks
Using Cookiecutter templates, projects can be spun up quickly whenever needed.
Using Cookiecutter templates, projects can be spun up quickly whenever needed.
Keep all parts of a project cleanly separated: data, code, configuration, documentation.
Automate data and code syncing to take the guesswork out of storage and backup.
For Python 3. If you do not have Python 3 installed on your machine, get the latest version here.
More detailed installation documents are available here.
This is our most popular plugin and sets you up nicely to use other plugins in the future if you want.
DataKit uses Cookiecutter templates for project structure and initial configuration.
The following templates are available:
pipenv
..Rproj
file.README
, and a .gitignore
file. If your project workflow isn't covered by the Python or R project templates, or you want to develop your own project template, this is the place to start.On the command line, datakit project create
will create a project with a standardized file structure.
Additional plugins can help you manage the storage of flat data files, sync your code to GitLab or GitHub and push your output to data.world for sharing. Grab other plugins or develop your own!
Data storage and backup: Amazon S3 sync - datakit-data
Version control:
GitHub integration - datakit-github
GitLab integration - datakit-gitlab
Publishing shareable output: Data.world - datakit-dworld
Useful DataKit plugins from around the community:
Data storage and backup:
Google Drive - datakit-data-gdrive
Version control:
Bitbucket - datakit-data-bitbucket